Many electronic and optical devices are currently produced using planar fabrication processes wherein one or more devices are fabricated on a planar substrate. Planar fabrication processes typically include a sequence of process steps. Such process steps can include thin-film or thick-film deposition, chemical and/or mechanical polishing, diffusion of dopants, and metalization, for example. Commonly, it is necessary to perform an alignment step after a process step. For example, an alignment step is required to return to the same location on a substrate to perform a measurement after a process step, as in a wafer prober, or a film thickness measurement system, or a flatness probe station. Another example is the alignment step required to register a lithography mask with respect to a previously fabricated pattern on the surface of the substrate, as in a wafer stepper.
However, a process step can cause a change in the appearance of the nascent device and/or the substrate upon which the device is being fabricated. Such changes can result in, for example, a non-uniform change in brightness over the area of an image of the device and/or substrate. That is to say, there is no single linear transformation of the brightness of each pixel in the pre-process-step image that will provide the post-process-step image. Instead, two or more different transformations are applied over respective sub-areas of the image to provide the post-process-step image. When confronted with images exhibiting non-uniform changes in pixel brightness between process steps, alignment based on such images can become problematic using known automated vision-based alignment methods, such as normalized correlation search.
Note that non-uniform changes in pixel brightness across a sequence of images of an object could also result from a change in lighting conditions over the sequence of images.
FIG. 1 illustrates a sample reference feature both before and after 10' a hypothetical process step that causes a non-uniform change in image brightness (gray value). The gray values G0, G1, and G2 of the three regions 12, 14, and 16, respectively, of the image 10 are shown, and the corresponding magnitude of the first difference (rate of change of gray value with position) plot 18 across the image is indicated immediately below.
For comparison, after a hypothetical substrate processing step, such as a semiconductor wafer process step or thin film process step, the gray values G1, G0, and G2 of each of three regions 12', 14' and 16' (where the prime (') indicates a post-process image) of the image 10' are shown, and the corresponding magnitude of the first difference 18' taken across the image is indicated immediately below. Thus, each process step can introduce a change in image polarity over the area of an image, or other non-uniform change in gray value over the image area, which is manifest as a non-uniform transformation over the first difference of the image. The change in the first difference image is non-uniform in that no single linear transformation over the entire pre-process first difference image can be found that can provide the post-process version of the first difference image. It is possible, however, to find a piece-wise linear or non-uniform linear transformation.
Such non-uniform changes in the first difference of the image can introduce further difficulty for some known search techniques, and particularly for search methods that employ edge thresholding, wherein only strong edges that exceed a threshold are deemed valid. This is because some substrate processing steps can reduce the strength of otherwise valid edges, and such edge information is not available to facilitate image searching.
Further, substrate processing steps can cause non-uniform changes in image gray values over the area of an image of the substrate that can introduce further difficulty for known search techniques, and particularly for techniques that employ normalized correlation search based solely on correlation of areas of image gray values.
In an attempt to overcome this difficulty, the SEARCH TOOL, an application of normalized correlation search sold by Cognex Corporation, Natick, Mass., requires that a different search model of the reference feature be trained for use after each process step, whenever each process step can change the appearance of the reference feature on the wafer in a non-uniform manner. However, this approach is impractical, because each processing step can have an unpredictable effect on the brightness, contrast, location, orientation, and/or scale of the reference feature, making it difficult to train a suitable sequence of search models. Moreover, even if a stable sequence of search models could be found for finding an image feature throughout a sequence of process steps, such an approach is time consuming and costly.
It is also known to compute a Sobel magnitude image of the reference feature on the wafer after each step of a sequence of process steps, and search the Sobel magnitude image using a Sobel magnitude image of the model image. However, in this case, the Sobel magnitude image changes in a non-uniform or non-linear manner after each process step. Further, the Sobel magnitude image may not work well in conjunction with some implementations of normalized correlation search. To overcome this problem, custom application-specific image pre-processing must be employed. The result is not necessarily adequate for many applications.